641 citations · 1.5k across the 13 of their papers we have counts for
5 papers · 1 filter
What do we need to build explainable AI systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis +1
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous dri…
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Andreas Holzinger, Bernd Malle, Peter Kieseberg +4
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the…
Augmentor: An Image Augmentation Library for Machine Learning
Marcus D. Bloice, Christof Stocker, Andreas Holzinger
The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and…
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop
Andreas Holzinger, Markus Plass, Katharina Holzinger +3
The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressi…
Review of Machine Learning Algorithms in Differential Expression Analysis
Irina Kuznetsova, Yuliya V Karpievitch, Aleksandra Filipovska +2
In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, pr…